Databricks MCP Server

by JustTryAI

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Python

Remote

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Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.2/5.0

Scored 2026-04-18 11:05 across 11 tools

Disambiguation5/5

Each tool has a distinct purpose with clear boundaries: cluster management (create, get, list, start, terminate), SQL execution, notebook operations (export, list), file listing, and job operations (list, run). No overlapping functionality exists, making tool selection straightforward for an agent.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern (e.g., create_cluster, execute_sql, list_files) using snake_case throughout. This predictable naming convention enhances readability and usability for agents.

Tool Count5/5

With 11 tools, the server is well-scoped for Databricks operations, covering clusters, SQL, notebooks, files, and jobs. Each tool serves a clear purpose without redundancy, making the count appropriate for the domain.

Completeness4/5

The toolset provides strong coverage for core Databricks workflows, including cluster lifecycle, SQL execution, notebook/file management, and job operations. Minor gaps exist, such as missing notebook creation or job update tools, but agents can work around these with available operations.